Swarm intelligence is the study of computational systems inspired by the 'collective intelligence'. Collective Intelligence emerges through the cooperation of large numbers of homogeneous agents in the environment. Examples include schools of fish, flocks of birds, and colonies of ants. Such intelligence is decentralized, self-organizing and distributed through out an environment. In nature such systems are commonly used to solve problems such as effective foraging for food, prey evading, or colony re-location. The information is typically stored throughout the participating homogeneous agents, or is stored or communicated in the environment itself such as through the use of pheromones in ants, dancing in bees, and proximity in fish and birds.

The paradigm consists of two dominant sub-fields 1) Ant Colony Optimization that investigates probabilistic algorithms inspired by the stigmergy and foraging behavior of ants, and 2) Particle Swarm Optimization that investigates probabilistic algorithms inspired by the flocking, schooling and herding. Like evolutionary computation, swarm intelligence 'algorithms' or 'strategies' are considered adaptive strategies and are typically applied to search and optimization domains.

Seminal books on the field of Swarm Intelligence include "Swarm Intelligence" by Kennedy, Eberhart and Shi [Kennedy2001], and "Swarm Intelligence: From Natural to Artificial Systems" by Bonabeau, Dorigo, and Theraulaz [Bonabeau1999]. Another excellent text book on the area is "Fundamentals of Computational Swarm Intelligence" by Engelbrecht [Engelbrecht2006]. The seminal book reference for the field of Ant Colony Optimization is "Ant Colony Optimization" by Dorigo and Stützle [Dorigo2004].